Although Business Intelligence (BI) applications are helping an increasing number of people to make decisions, running the BI applications to do business analysis often results in performance problems. Too much time and system resources are spent on the large amount of data in a data warehouse prepared for running a report. Further, because there are too many reports and data, an excessive amount of time and system resources are spent on analyzing and finding out information that has value. In known report delivery systems, users may log onto a portal and run reports and then analyze data in the reports, or a timer may be specified to run a specific report template at a scheduled time. Static pre-fetch technology such as Materialized Query Table is used for acceleration in a BI system, which is based on the assumption of data popularity and the pre-fetched data is always fixed summary data for specific metrics. Static pre-fetch technology is built along with the data warehouse schema and is not changed frequently, thereby leading to out-dated reports that do not match updated user interests (e.g., user interests that may change over period of time due to a user role change or market trend and other external factors). The aforementioned techniques are causing performance issues if there are many users logged onto the portal and running reports at the same time. Running reports at a specific scheduled time may also be slow because of the tendency to schedule reports to run at night. Thus, there exists a need to overcome at least one of the preceding deficiencies and limitations of the related art.